scholarly journals Synaptic learning rules for sequence learning

2020 ◽  
Author(s):  
Eric T. Reifenstein ◽  
Richard Kempter
Keyword(s):  
Sequence Learning ◽  
Time Scale ◽  
Temporal Order ◽  
Fast Time ◽  
Sequence Of Events ◽  
Neuronal Mechanisms ◽  
Phase Precession ◽  
Millisecond Time Scale ◽  
Neuronal Processing ◽  
Synaptic Learning

AbstractRemembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity—termed “phase precession”—enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that phase precession can improve sequence learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

scholarly journals Synaptic learning rules for sequence learning

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eric Torsten Reifenstein ◽  
Ikhwan Bin Khalid ◽  
Richard Kempter
Keyword(s):  
Sequence Learning ◽  
Time Scale ◽  
Temporal Order ◽  
Fast Time ◽  
Sequence Of Events ◽  
Neuronal Mechanisms ◽  
Phase Precession ◽  
Millisecond Time Scale ◽  
Neuronal Processing ◽  
Synaptic Learning

Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

scholarly journals Dichotomy in temporal and thermal spin correlations observed in the breathing pyrochlore LiGa1−xInxCr4O8

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
S. Lee ◽  
S.-H. Do ◽  
W. Lee ◽  
Y. S. Choi ◽  
J. van Tol ◽  
...  
Keyword(s):  
Time Scale ◽  
Spin Dynamics ◽  
Muon Spin ◽  
Quantum Spin ◽  
Spin Liquid ◽  
Fast Time ◽  
Spin Liquids ◽  
Magnetic Ground State ◽  
Spin Correlations ◽  
Magnetic Specific Heat

AbstractA breathing pyrochlore system is predicted to host a variety of quantum spin liquids. Despite tremendous experimental and theoretical efforts, such sought-after states remain elusive as perturbation terms and lattice distortions lead to magnetic order. Here, we utilize bond alternation and disorder to tune a magnetic ground state in the Cr-based breathing pyrochlore LiGa1−xInxCr4O8. By combining thermodynamic and magnetic resonance techniques, we provide experimental signatures of a spin-liquid-like state in x = 0.8, namely, a nearly T2-dependent magnetic specific heat and persistent spin dynamics by muon spin relaxation (μSR). Moreover, 7Li NMR, ZF-μSR, and ESR unveil the temporal and thermal dichotomy of spin correlations: a tetramer singlet on a slow time scale vs. a spin-liquid-like state on a fast time scale. Our results showcase that a bond disorder in the breathing pyrochlore offers a promising route to disclose exotic magnetic phases.

Visual Simultaneity Judgments Activate a Bilateral Frontoparietal Timing System

2019 ◽  
Vol 31 (3) ◽  
pp. 431-441 ◽  
Author(s):  
Taylor Hanayik ◽  
Grigori Yourganov ◽  
Roger Newman-Norlund ◽  
Makayla Gibson ◽  
Chris Rorden
Keyword(s):  
Temporal Processing ◽  
Temporal Order ◽  
Spatial Information ◽  
Right Hemisphere ◽  
Temporal Order Judgment ◽  
Inferior Frontal Gyrus ◽  
Stimulus Presentation ◽  
Cognitive Networks ◽  
Temporal Perception ◽  
Sequence Of Events

In everyday life, we often make judgments regarding the sequence of events, for example, deciding whether a baseball runner's foot hit the plate before or after the ball hit the glove. Numerous studies have examined the functional correlates of temporal processing using variations of the temporal order judgment and simultaneity judgment (SJ) tasks. To perform temporal order judgment tasks, observers must bind temporal information with identity and/or spatial information relevant to the task itself. SJs, on the other hand, require observers to detect stimulus asynchrony but not the order of stimulus presentation and represent a purer measure of temporal processing. Some previous studies suggest that these temporal decisions rely primarily on right-hemisphere parietal structures, whereas others provide evidence that temporal perception depends on bilateral TPJ or inferior frontal regions (inferior frontal gyrus). Here, we report brain activity elicited by a visual SJ task. Our methods are unique given our use of two orthogonal control conditions, discrimination of spatial orientation and color, which were used to control for brain activation associated with the classic dorsal (“where/how”) and ventral (“what”) visual pathways. Our neuroimaging experiment shows that performing the SJ task selectively activated a bilateral network in the parietal (TPJ) and frontal (inferior frontal gyrus) cortices. We argue that SJ tasks are a purer measure of temporal perception because they do not require observers to process either identity or spatial information, both of which may activate separate cognitive networks.

Sensing oxygen at the millisecond time-scale using an ultra-microelectrode array (UMEA)

2017 ◽  
Vol 238 ◽  
pp. 1008-1016 ◽  
Author(s):  
Fleur van Rossem ◽  
Johan G. Bomer ◽  
Hans L. de Boer ◽  
Yawar Abbas ◽  
Eddy de Weerd ◽  
...  
Keyword(s):  
Time Scale ◽  
Microelectrode Array ◽  
Millisecond Time Scale

A New Model for Long-Term Stochastic Analysis and Prediction—Part I: Theoretical Background

1992 ◽  
Vol 36 (01) ◽  
pp. 1-16
Author(s):  
G. A. Athanassoulis ◽  
P. B. Vranas ◽  
T. H. Soukissian
Keyword(s):  
Time Scale ◽  
Random Function ◽  
Spectral Characteristics ◽  
Time History ◽  
Time Variable ◽  
Sea Surface ◽  
Surface Elevation ◽  
Fast Time ◽  
Slow Time

A new approach for calculating the long-term statistics of sea waves is proposed. A rational long-term stochastic model is introduced which recognizes that the wave climate at a given site in the ocean consists of a random succession of individual sea states, each sea state possessing its own duration and intensity. This model treats the sea-surface elevation as a random function of a "fast" time variable, and the time history of the spectral characteristics of the successive sea states as a random function of a "slow" time variable. By developing an appropriate conceptual framework, it becomes possible to express various probabilistic characteristics of the sea-surface elevation, which are sensible only in the fast-time scale, in terms of the statistics of sea-states duration and intensity, which is meaningful only in the slow-time scale. As an example, we study the random quantity MU(T) = "number of maxima of the sea-surface elevation lying above the level u and occurring during a long-term time period [0,T]." Exploiting the proposed framework, it is shown that, under certain clearly defined assumptions, Mu(T) can be given the structure of a renewal-reward (cumulative) process, whose interarrival times correspond to the duration of successive sea states. Thus, using renewal theory, the complete characterization of the probability structure of MU(T) is obtained. As a consequence, the long-term probability distribution function of the individual wave height is rigorously defined and calculated. The relation of the present results with corresponding ones previously obtained is thoroughly discussed. The proposed model can be extended twofold: either by replacing some of the simplifying assumptions by more realistic ones, or by extending the model for treating the corresponding problems for ship and structures responses.

Parameter Estimation in a Nonlinear Vibrating System Using an Observer for an Extended System

1999 ◽  
Author(s):  
Anindya Chatterjee ◽  
Joseph P. Cusumano
Keyword(s):  
Parameter Estimation ◽  
Time Scale ◽  
Asymptotic Methods ◽  
System Response ◽  
Parameter Estimates ◽  
Fast Time ◽  
State Variables ◽  
Slow Time ◽  
Unknown Parameters ◽  
Lipschitz Continuous

Abstract We present a new observer-based method for parameter estimation for nonlinear oscillatory mechanical systems where the unknown parameters appear linearly (they may each be multiplied by bounded and Lipschitz continuous but otherwise arbitrary, possibly nonlinear, functions of the oscillatory state variables and time). The oscillations in the system may be periodic, quasiperiodic or chaotic. The method is also applicable to systems where the parameters appear nonlinearly, provided a good initial estimate of the parameter is available. The observer requires measurements of displacements. It estimates velocities on a fast time scale, and the unknown parameters on a slow time scale. The fast and slow time scales are governed by a single small parameter ϵ. Using asymptotic methods including the method of averaging, it is shown that the observer’s estimates of the unknown parameters converge like e−ϵt where t is time, provided the system response is such that the coefficient-functions of the unknown parameters are not close to being linearly dependent. It is also shown that the method is robust in that small errors in the model cause small errors in the parameter estimates. A numerical example is provided to demonstrate the effectiveness of the method.

scholarly journals Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics

2012 ◽  
Vol 8 (9) ◽  
pp. 2997-3002 ◽  
Author(s):  
Levi C.T. Pierce ◽  
Romelia Salomon-Ferrer ◽  
Cesar Augusto F. de Oliveira ◽  
J. Andrew McCammon ◽  
Ross C. Walker
Keyword(s):  
Molecular Dynamics ◽  
Time Scale ◽  
Accelerated Molecular Dynamics ◽  
Millisecond Time Scale

scholarly journals Directly monitor protein rearrangement on a nanosecond-to-millisecond time-scale

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Eric H.-L. Chen ◽  
Tony T.-Y. Lu ◽  
Jack C.-C. Hsu ◽  
Yufeng Jane Tseng ◽  
T.-S. Lim ◽  
...  
Keyword(s):  
Time Scale ◽  
Millisecond Time Scale

The effect of fast-time-scale turbulence on magnetohydrodynamical behaviour

1984 ◽  
Vol 31 (1) ◽  
pp. 81-92 ◽  
Author(s):  
R. O. Dendy ◽  
D. Ter Haar
Keyword(s):  
Time Scale ◽  
Normal Modes ◽  
Dispersion Relations ◽  
Magnetized Plasma ◽  
Fast Time ◽  
Fast Time Scale ◽  
Scale Turbulence ◽  
The Ideal

We show what corrections have to be made to the equations of ideal magneto-hydrodynamics when there is fast-time-scale turbulence present in a magnetized plasma. We show how the dispersion relations for the ideal Alfvén and magnetoacoustic MHD normal modes are modified when such turbulence is present. Finally, we discuss the relation of our work to that of other authors.

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